Responses of forest herbs to available understory light measured with hemispherical photographs in silver fir–beech forest in Croatia

• Published in: Ecological modelling 2006-03, Vol.194 (1), p.209-218
• Main Authors: Jelaska, Sven D., Antonić, Oleg, Božić, Mario, Križan, Josip, Kušan, Vladimir
• Format: Article
• Language: English
• Subjects: Abies alba; Animal, plant and microbial ecology; Biological and medical sciences; Canopy openness; Digital terrain model; Fagus sylvatica; Forestry; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; General forest ecology; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology; LAI; Methods and techniques (sampling, tagging, trapping, modelling...); NDVI; Omphalodo-Fagetum
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2005.10.013

In this research we investigated response of forest herbs to available light conditions in the forest of silver fir ( Abies alba) and beech ( Fagus sylvatica) which are most frequent altimontane forest on Croatian karst, using the hemispherical photographs for measuring available light. In addition to this, we tested the possibility of estimation light condition parameters on the large areas using the remotely sensed data, as potential support to the spatial prediction of phytodiversity and particular plant species occurrences. Data about the floristic assemblage were gathered at two spatial resolution at each location consisted of 5 and 12.5 m radius circular plots. Hemispherical photographs of forest canopies were taken in the centre of each plot and analyzed with Gap Light Analyzer software calculating the two canopy characteristics: leaf area index (LAI) and canopy openness. For majority of sampled area satellite Landsat ETM+ image was available which enable comparison of LAI with spectral channels and Normalized Difference Vegetation Index (NDVI). Relations between mentioned variables (floristic, derived from hemispherical photographs and derived from satellite image) were investigated by redundancy analyses (RDA), classification trees (CT), logistic and linear multiple regression. Using the RDA, six herbaceous plant species that are most influenced by the light conditions were detected. According to CT analysis that included altitude, slope, northness (cosine of aspect) and LAI as predictor variables, LAI was important predictor variable for estimating the frequency of particular plant species. Besides CT, predictive logistic regression models were developed for selected species as well. Regression models developed with Landsat ETM+ bands as estimators explained moderate amount of variability in LAI data. Obtained results lead to the future modelling of silver fir–beech forest phytodiversity and spatial distribution of particular plant species as a function of environmental estimators.